p = figure(tools='box_select')
source = GeoJSONDataSource(geojson=original)
p.circle(line_color=None, fill_alpha=0.8, source=source)
# Open a session which will keep our local doc in sync with server
session = push_session(curdoc())
# Open the session in a browser
session.show()
is_original = True
while True:
if is_original:
# update to something else
source.geojson = json.dumps(
{'type': 'FeatureCollection',
'features': [
{"type": "Feature",
"geometry": {"type": "Point",
"coordinates": [-2.1208465099334717, 51.4613151550293]},
"properties": {"OrganisationCode": "Q64"}}
]}
)
is_original = False
else:
# set back to original
source.geojson = original
is_original = True
time.sleep(.5)
tplot.add_layout(text_val5)
tplot.add_layout(text_val6)
tplot.add_layout(text_rate)
tplot.add_layout(text_val7)
tplot.add_layout(text_val8)
tplot.add_layout(text_adults)
tplot.add_layout(text_val9)
tplot.add_layout(text_val10)
tplot.add_layout(text_capita)
tplot.add_layout(text_val11)
tplot.add_layout(text_val12)
def update_plot(attr, old, new):
yr = slider.value
new_data = get_json(str(yr), None)[0]
geosource.geojson = new_data
plot.title.text = 'State Craft Beer Sales, %d' %yr
# Make a slider object: slider
slider = Slider(title = 'Year',start = 2008, end = 2018, step = 1, value = 2018)
slider.on_change('value', update_plot)
def make_dataset(company_list):
xs = []
ys = []
colors = []
labels = []
for i, company in enumerate(company_list):
df_co = df_companies.loc[df_companies['company']==company]
df_co = df_co.drop(['company', 'state'], axis=1)
list_x = [int(i) for i in list(df_co)]
list_y = df_co.values[0]
xs.append(list_x)
# if "1990s Shrubland" in add_this:
# shrub_1990_geosource.geojson = shrub_1990_json
# elif "1990s Shrubland" in remove_this:
# shrub_1990_geosource.geojson = blank_json_data
if "2010s Chaparral" in add_this:
landfire_geosource.geojson = landfire_json
elif "2010s Chaparral" in remove_this:
landfire_geosource.geojson = blank_json_data
old_list = new_list
### Set the layers to blank for the initial map
alt_geosource.geojson = blank_json_data
dMAT_geosource.geojson = blank_json_data_dmat #because of the color fill, it's important to have the own blank here
chap_1930_geosource.geojson = blank_json_data
# shrub_1990_geosource.geojson = blank_json_data
landfire_geosource.geojson = blank_json_data
###Prep Color Bars for the environmental variables
def make_colorbar(shapefile_values, color_value_string, color_bar_title):
#get the min and max values of shapefile
min_range = min(shapefile_values)
max_range = max(shapefile_values)
palette = brewer[color_value_string][
#th=list(set(purpose['C']))
#th=[str(i) for i in th]
#mapper=factor_cmap('C', RdYlGn[5],th )
#mapper=linear_cmap('C', RdYlGn[5], 0, max(purpose.C))
color_mapper = LinearColorMapper(palette=RdYlGn[5])
#color_mapper.high=max(df_aggreg['value'])
#color_mapper.low=min(df_aggreg['value'])
geo_source = GeoJSONDataSource()
geo_source.geojson=json.dumps({"type":"FeatureCollection","features":[{"type":"Feature","id":"832830fffffffff","geometry":{"type":"Polygon","coordinates":[[[] for x in range(1)]]},"properties":{'Hex_Count': 1, 'Hex_No': '27'}}]})
p.patches('xs', 'ys', fill_alpha=0.7, fill_color={'field': 'Hex_Count', 'transform': color_mapper},
line_color='white', line_width=0.5, source=geo_source)
color_bar = ColorBar(color_mapper=color_mapper, ticker= BasicTicker(),
location=(0,0))
#color_bar.click_policy="hide"
p.add_layout(color_bar, 'right')
#output_file("hex_tile.html")
def output_res():
with open(r'./twgeo_easy_rates.json', 'r') as file:
json_str = file.read()
geo_source = GeoJSONDataSource(geojson=json_str)
twgeo_json = json.loads(json_str)
pred_4week = pd.read_csv('prediction_4week.csv')
areas = ['南區', '北區', '東區', '高屏區', '台北區', '中區']
models = ['CatBoost', 'XGB', 'ConvLSTM', 'GRU', 'LGBM', 'truth']
color_list = ['black', 'red', 'blue', 'green', 'cyan', 'purple']
area_dict = {}
for idx, area in enumerate(areas):
area_dict[area] = {}
temp = pd.read_csv('./data/{}.csv'.format(area))
area_dict[area]['df'] = temp
area_dict[area]['DT'] = pd.to_datetime(temp['DT'].values)
area_idx = []
for i in range(22):
if twgeo_json['features'][i]['properties']['area'] == area:
area_idx.append(i)
area_dict[area]['area_idx'] = area_idx
def fig_config(fig):
fig.grid.grid_line_color = None
fig.toolbar.logo = None
fig.toolbar_location = None
fig.legend.location = "top_left"
fig.legend.click_policy = "hide"
fig.xaxis.axis_label = 'Date'
fig.yaxis.axis_label = '腸病毒人數'
future_4_weeks = []
data_dict = {}
model_cds = {}
pred_4week = pd.read_csv('prediction_4week.csv')
pred_64week = pd.read_csv('prediction_64week.csv')
pred_4week_mae = pd.read_csv('mae_4week.csv')
pred_64week_mae = pd.read_csv('mae_64week.csv')
for area in areas:
for model in models:
data_dict['{}_{}_4week'.format(area,
model)] = pred_4week['{}_{}'.format(
area, model)].values
data_dict['{}_{}_64week'.format(
area, model)] = pred_64week['{}_{}'.format(area, model)].values
data_dict['{}_{}_4week_mae'.format(area, model)] = pred_4week_mae
data_dict['x_4week'] = pd.to_datetime(pred_4week['x'].values)
data_dict['x_64week'] = pd.to_datetime(pred_64week['x'].values)
dates = data_dict['x_64week']
fig_predict = figure(title="predictions",
tools=TOOLS,
width=600,
height=300,
x_axis_type='datetime')
mae_source_4week = ColumnDataSource(data=dict())
mae_source_64week = ColumnDataSource(data=dict())
columns = [
TableColumn(field="model", title="model Name"),
TableColumn(field="mae", title="MAE")
]
data_table_4week = DataTable(source=mae_source_4week,
columns=columns,
width=800)
data_table_64week = DataTable(source=mae_source_64week,
columns=columns,
width=800)
stats_4week = PreText(text='', width=500)
stats_64week = PreText(text='', width=500)
fig_predict_1year = figure(title="predictions_1year",
tools=TOOLS,
width=1000,
height=300,
x_axis_type='datetime',
x_axis_location="above",
background_fill_color="#efefef",
x_range=(dates[0], dates[20]))
select = figure(
title=
"Drag the middle and edges of the selection box to change the range above",
plot_height=130,
plot_width=1000,
y_range=fig_predict_1year.y_range,
x_axis_type="datetime",
y_axis_type=None,
tools="",
toolbar_location=None,
background_fill_color="#efefef")
range_rool = RangeTool(x_range=fig_predict_1year.x_range)
range_rool.overlay.fill_color = "navy"
range_rool.overlay.fill_alpha = 0.2
for i, model in enumerate(models):
model_cds[model] = {}
model_cds[model]['cds_4week'] = ColumnDataSource(data={
'x': [],
'y': []
})
model_cds[model]['cds_64week'] = ColumnDataSource(data={
'x': [],
'y': []
})
fig_predict.line(x='x',
y='y',
source=model_cds[model]['cds_4week'],
line_color=color_list[i],
muted_color=color_list[i],
line_width=2,
line_alpha=0.7,
legend=model)
fig_predict_1year.line(x='x',
y='y',
source=model_cds[model]['cds_64week'],
line_color=color_list[i],
muted_color=color_list[i],
line_width=2,
line_alpha=0.7,
legend=model)
select.line('x',
'y',
source=model_cds[model]['cds_64week'],
line_color=color_list[i],
muted_color=color_list[i])
select.ygrid.grid_line_color = None
select.add_tools(range_rool)
select.toolbar.active_multi = range_rool
fig_config(fig_predict)
fig_config(fig_predict_1year)
p = figure(title="Taiwan",
tools=TOOLS,
x_axis_location=None,
y_axis_location=None,
width=600,
height=800)
p.background_fill_color = 'beige'
p.border_fill_color = 'black'
p.border_fill_alpha = 0.05
p.grid.grid_line_color = None
p.toolbar.logo = None
p.toolbar_location = None
for i in range(22):
twgeo_json['features'][i]['properties']['line_width'] = 0.5
twgeo_json['features'][i]['properties']['line_color'] = 'white'
geo_source.geojson = json.dumps(twgeo_json)
selected_g = Patches(fill_color='blue',
line_color='white',
line_width=4,
line_alpha=0.5)
renderer = p.patches('xs',
'ys',
fill_alpha=0.7,
fill_color='blue',
line_color='line_color',
line_width='line_width',
line_alpha=1,
source=geo_source)
renderer.selection_glyph = selected_g
p.add_tools(TapTool())
p.add_tools(
HoverTool(tooltips=[("name",
"@name"), ("index",
"$index"), ("(Lon,Lat)", "($x, $y)")]))
def my_tap_handler(attr, old, new):
fig_config(fig_predict)
idx = geo_source.selected.indices[0]
area = twgeo_json['features'][idx]['properties']['area']
geo_source.selected.indices = area_dict[area]['area_idx']
for i in range(22):
if twgeo_json['features'][i]['properties']['area'] == area:
twgeo_json['features'][i]['properties']['line_width'] = 4
twgeo_json['features'][i]['properties']['line_color'] = 'black'
else:
twgeo_json['features'][i]['properties']['line_width'] = 0.5
twgeo_json['features'][i]['properties']['line_color'] = 'white'
geo_source.geojson = json.dumps(twgeo_json)
if area:
temp_date_4week = data_dict['x_4week']
temp_date_64week = data_dict['x_64week']
temp_text_4week = ''
for i, model in enumerate(models):
temp_y_4week = data_dict['{}_{}_4week'.format(area, model)]
temp_y_64week = data_dict['{}_{}_64week'.format(area, model)]
model_cds[model]['cds_4week'].data = {
'x': temp_date_4week,
'y': temp_y_4week
}
model_cds[model]['cds_64week'].data = {
'x': temp_date_64week,
'y': temp_y_64week
}
stats_4week.text = area + '(last 4 weeks mae):\n'
stats_64week.text = area + '(last one year mae):\n'
mae_source_4week.data = {
'model': [model for model in models if model != 'truth'],
'mae': [
pred_4week_mae['{}_{}'.format(area, model)]
for model in models if model != 'truth'
],
}
mae_source_64week.data = {
'model': [model for model in models if model != 'truth'],
'mae': [
pred_64week_mae['{}_{}'.format(area, model)]
for model in models if model != 'truth'
],
}
geo_source.on_change('selected', my_tap_handler)
layouts = column(fig_predict, stats_4week, data_table_4week)
layouts = row(p, layouts)
layouts = column(layouts, fig_predict_1year, select, stats_64week,
data_table_64week)
return layouts
if "1930s Chaparral" in add_this:
chap_1930_geosource.geojson = chap_1930_json
elif "1930s Chaparral" in remove_this:
chap_1930_geosource.geojson = blank_json_data
if "1990s Shrubland" in add_this:
shrub_1990_geosource.geojson = shrub_1990_json
elif "1990s Shrubland" in remove_this:
shrub_1990_geosource.geojson = blank_json_data
old_list = new_list
### Set the layers to blank for the initial map
alt_geosource.geojson = blank_json_data
dMAT_geosource.geojson = blank_json_data_dmat #because of the color fill, it's important to have the own blank here
chap_1930_geosource.geojson = blank_json_data
shrub_1990_geosource.geojson = blank_json_data
###Prep Color Bars for the environmental variables
def make_colorbar(shapefile_values, color_value_string, color_bar_title):
#get the min and max values of shapefile
min_range = min(shapefile_values)
max_range = max(shapefile_values)
palette = brewer[color_value_string][
8] #Define a sequential multi-hue color palette.
